randomForestSRC-package |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
breast |
Wisconsin Prognostic Breast Cancer Data |
extract.bootsample |
Subsample Forests for VIMP Confidence Intervals |
extract.quantile |
Quantile Regression Forests |
extract.subsample |
Subsample Forests for VIMP Confidence Intervals |
find.interaction |
Find Interactions Between Pairs of Variables |
find.interaction.rfsrc |
Find Interactions Between Pairs of Variables |
follic |
Follicular Cell Lymphoma |
get.auc |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.bayes.rule |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.brier.error |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.brier.survival |
Plot of Survival Estimates |
get.cindex |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.confusion |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.imbalanced.optimize |
Imbalanced Two Class Problems |
get.imbalanced.performance |
Imbalanced Two Class Problems |
get.logloss |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.misclass.error |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.cserror |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.csvimp |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.error |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.error.block |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.formula |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.predicted |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.mv.vimp |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
get.partial.plot.data |
Acquire Partial Effect of a Variable |
get.pr.auc |
Imbalanced Two Class Problems |
get.pr.curve |
Imbalanced Two Class Problems |
get.quantile |
Quantile Regression Forests |
get.quantile.crps |
Quantile Regression Forests |
get.quantile.stat |
Quantile Regression Forests |
get.rfq.threshold |
Imbalanced Two Class Problems |
get.tree |
Extract a Single Tree from a Forest and plot it on your browser |
get.tree.rfsrc |
Extract a Single Tree from a Forest and plot it on your browser |
hd |
Hodgkin's Disease |
holdout.vimp |
Hold out variable importance (VIMP) |
holdout.vimp.rfsrc |
Hold out variable importance (VIMP) |
housing |
Ames Iowa Housing Data |
imbalanced |
Imbalanced Two Class Problems |
imbalanced.rfsrc |
Imbalanced Two Class Problems |
impute |
Impute Only Mode |
impute.rfsrc |
Impute Only Mode |
max.subtree |
Acquire Maximal Subtree Information |
max.subtree.rfsrc |
Acquire Maximal Subtree Information |
nutrigenomic |
Nutrigenomic Study |
partial |
Acquire Partial Effect of a Variable |
partial.rfsrc |
Acquire Partial Effect of a Variable |
pbc |
Primary Biliary Cirrhosis (PBC) Data |
peakVO2 |
Systolic Heart Failure Data |
plot.competing.risk |
Plots for Competing Risks |
plot.competing.risk.rfsrc |
Plots for Competing Risks |
plot.quantreg |
Plot Quantiles from Quantile Regression Forests |
plot.quantreg.rfsrc |
Plot Quantiles from Quantile Regression Forests |
plot.rfsrc |
Plot Error Rate and Variable Importance from a RF-SRC analysis |
plot.subsample |
Plot Subsampled VIMP Confidence Intervals |
plot.subsample.rfsrc |
Plot Subsampled VIMP Confidence Intervals |
plot.survival |
Plot of Survival Estimates |
plot.survival.rfsrc |
Plot of Survival Estimates |
plot.variable |
Plot Marginal Effect of Variables |
plot.variable.rfsrc |
Plot Marginal Effect of Variables |
predict.rfsrc |
Prediction for Random Forests for Survival, Regression, and Classification |
print.bootsample |
Subsample Forests for VIMP Confidence Intervals |
print.bootsample.rfsrc |
Subsample Forests for VIMP Confidence Intervals |
print.rfsrc |
Print Summary Output of a RF-SRC Analysis |
print.subsample |
Subsample Forests for VIMP Confidence Intervals |
print.subsample.rfsrc |
Subsample Forests for VIMP Confidence Intervals |
quantreg |
Quantile Regression Forests |
quantreg.rfsrc |
Quantile Regression Forests |
randomForestSRC |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
rfsrc |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
rfsrc.anonymous |
Anonymous Random Forests |
rfsrc.cart |
Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC) |
rfsrc.fast |
Fast Random Forests |
rfsrc.news |
Show the NEWS file |
sid.perf.metric |
sidClustering using SID (Staggered Interaction Data) for Unsupervised Clustering |
sidClustering |
sidClustering using SID (Staggered Interaction Data) for Unsupervised Clustering |
sidClustering.rfsrc |
sidClustering using SID (Staggered Interaction Data) for Unsupervised Clustering |
stat.split |
Acquire Split Statistic Information |
stat.split.rfsrc |
Acquire Split Statistic Information |
subsample |
Subsample Forests for VIMP Confidence Intervals |
subsample.rfsrc |
Subsample Forests for VIMP Confidence Intervals |
synthetic |
Synthetic Random Forests |
synthetic.rfsrc |
Synthetic Random Forests |
tune |
Tune Random Forest for the optimal mtry and nodesize parameters |
tune.nodesize |
Tune Random Forest for the optimal mtry and nodesize parameters |
tune.nodesize.rfsrc |
Tune Random Forest for the optimal mtry and nodesize parameters |
tune.rfsrc |
Tune Random Forest for the optimal mtry and nodesize parameters |
var.select |
Variable Selection |
var.select.rfsrc |
Variable Selection |
vdv |
van de Vijver Microarray Breast Cancer |
veteran |
Veteran's Administration Lung Cancer Trial |
vimp |
VIMP for Single or Grouped Variables |
vimp.rfsrc |
VIMP for Single or Grouped Variables |
wihs |
Women's Interagency HIV Study (WIHS) |
wine |
White Wine Quality Data |